Advertisement

Protein-Based Stable Isotope Probing (Protein-SIP): Applications for Studying Aromatic Hydrocarbon Degradation in Microbial Communities

  • Nico Jehmlich
  • Martin von BergenEmail author
Reference work entry
  • 48 Downloads
Part of the Handbook of Hydrocarbon and Lipid Microbiology book series (HHLM)

Abstract

Protein-based stable isotope probing (protein-SIP) was developed to link microbial-specific metabolic function to phylogenetic information. The principle of the SIP-concept is the supplement of stable isotope-labelled compounds resulting in the labelling of microorganisms that are capable of utilizing these substrates as carbon source. The sum of all proteins reflects the functional status of the living organisms, so that the degree of heavy isotope incorporation represents a proxy for substrate assimilation and their activity. The main focus of this chapter is on the application of protein-SIP to elucidate metabolic processes in general and in particular those involved in the anaerobic degradation of aromatic hydrocarbons. Thus, the application of protein-SIP is a useful method to investigate the composition and the functional state of microbial communities.

1 Introduction

Meta-omics approaches allow a deep insight into the structure and function of microbial communities (Herbst et al. 2015). Metaproteomics also referred to as community proteomics or environmental proteomics has become over the last years metaproteomics, also referred to as community proteomics or environmental proteomics, has become an important tool in studying microbial ecology approaches (von Bergen et al. 2013) allowing to elucidate functional relationships between microbial community members (Bastida et al. 2015). However, metaproteome analysis alone cannot elucidate which protein and their corresponding species is currently active under specific conditions. For this reason, SIP techniques are required to solve this problem (Jehmlich et al. 2008a). During protein-SIP analysis, the metabolism of the labelled substrate can be detected with high precision on the peptide level. The identification of uniquely labelled peptides principally allows the assignment to phylotypes that express the encoding genes of the proteins providing a direct link of metabolic function to single members of the microbial community (Jehmlich et al. 2016). For these reasons, protein-SIP has developed into an important and widely used tool in microbial ecology. Since its development in 2008 (Jehmlich et al. 2008b), the spectrum of used stable isotopes in protein-SIP analyses has continuously broadened. By applying mass spectrometry (MS), the MS spectra provide biologically valuable information: (i) the presence/abundance of peptides/proteins, (ii) the primary amino acid sequence thereby revealing the proposed function, and (iii) the metabolic activity of the corresponding organism as a measure of incorporation of isotopes. Bioinformatics solutions for automatic data evaluation have been developed, and the range of application has been enlarged from microcosms over mesocosms up to in situ environmental sampling. This book chapter focuses on existing studies on protein-SIP and discusses potential trends of its application in environmental microbiology (e.g., labelled water) with a special focus on anaerobic degradation of aromatic hydrocarbons (Grob et al. 2015). Most technical aspects of protein-SIP applications have been reviewed previously (Taubert et al. 2011; von Bergen et al. 2013).

In the coming years, the development in the fields of mass spectrometry and bioinformatics will increase the resolution and specificity of protein-SIP applications. Further integration of comprehensive protein extraction procedures will promote the analysis of samples with lower amounts of protein, which opens the way to investigate natural microbial communities in more detail.

1.1 Protein-SIP Concept

Metabolic incorporation of heavy stable isotopes (usually 13C or 15N) into proteins has become a powerful technique for qualitative and quantitative proteome studies because the assessment of incorporation is a valuable parameter to investigate the general metabolic activity as well as protein turnover rates. In a time course experiment, the accurate quantification of incorporation in protein-SIP enables also the identification of food webs in microbial consortia.

There are two ways of metabolic protein labelling: (i) the use of substrates or nutrients such as [13C7]-glucose or 15N-ammonium which are biochemically incorporated into amino acids and (ii) the use of specifically labelled amino acids that are added to the medium (SILAC, for details see (Chen et al. 2015)). Stable isotope-labelled substrates (D, 13C, 15N, 18O, 34/36S, or nutrients) have become attractive since the mass shift measured by mass spectrometry clearly allows the quantitative assessment of isotope incorporation, and this is an indirect measurement of metabolic activity in respect to a specific substrate or nutrient. Two important parameters can be retrieved from the mass spectrometry data. First, the relative isotope abundance (RIA) describes the number of labelled atoms in a peptide and gives information about the proportion of labelled substrate that was assimilated. The second parameter named labelling ratio (LR) describes the ratio of labelled to natural peptide and refers to protein turnover rates after addition of the labelled substrate. The greatest advantages of protein-SIP are the accurate and very sensitive quantification of incorporation and the physiological information that is obtained concomitantly.

1.2 Calculation of Stable Isotope Incorporation into Peptides

In contrast to the incorporation of isotopically defined labelled amino acids (SILAC) which results in fixed mass shift (e.g., +4 Dalton mass shift), metabolic labelling using growth substrates or nutrients leads to dynamic and unpredictable mass shifts in the MS spectrum. In environmental studies, protein-SIP incorporation is often difficult due to the low intense and complex MS spectra, thereby making the data processing time-consuming and demanding. The process in automatic evaluation tools increased the feasibility of protein-SIP applications. Exemplarily, Pan et al. (2011) developed the Sipros algorithm (open-source and available http://code.google.com/p/sipros) to identify peptide sequences and quantify their 15N atom% composition. The abundance ratio between two isotopologues (heavy and light) of a protein was estimated in combination with ProRata (http://code.google.com/p/prorata/). Sachsenberg et al. (2015) developed MetaProSIP that calculates peptide relative isotope abundance for (i) 13C, 15N, deuterium (2H), and oxygen (18O), (ii) the labelling ratio (LR) between old and new synthesized proteins, and (iii) the shape of the isotopic distribution. Therefore, MetaProSIP provides high reliability and reproducibility which is combined with a quality reporting option (www.openms.de/MetaProSIP) (Sachsenberg et al. 2015). The MetaProSIP node was further implemented into Galaxy (homepage: https://galaxyproject.org, main public server: https://usegalaxy.org) which is a web-based scientific analysis platform to analyze genomics, proteomics, metabolomics, or imaging datasets (bioinformatics workflow in Galaxy to calculate incorporation Fig. 1) (Afgan et al. 2018). This allows a worldwide accessibility that will certainly encourage more groups to use labelled substrates to elucidate the functional behavior of microbial communities.
Fig. 1

Customized MetaProSIP workflow Galaxy that can be applied for the calculation of stable isotope incorporation into peptides/proteins

1.3 Protein-SIP Applications

There are several studies published that use protein-SIP in microbial ecology approaches. Exemplarily, we reviewed three applications that benchmark the field of protein-SIP, (i) a time-resolved protein-SIP, (ii) in situ protein-SIP studies, and (iii) labelled water to track general microbial activity.

A sulfate-reducing enrichment culture originating from a benzene-contaminated aquifer was investigated using time-resolved protein-SIP (Taubert et al. 2012). Benzene is a major contaminant in this aquifer, and the initial mechanisms behind its biodegradation under strictly anoxic conditions were not yet entirely clear. The carbon fluxes within the microbial community were investigated by addition of either 13C-benzene or 13C-carbonate. The utilization of the initial carbon source and the metabolic intermediates was relatively quantified, and the functional groups were affiliated to Clostridiales, Deltaproteobacteria, and Bacteroidetes/Chlorobi. Taubert et al. further observed that the Clostridiales-related organisms were involved in benzene degradation putatively by fermentation. This study revealed that protein-SIP can be applied in a time-dependent manner to obtain temporal and taxonomic information within a microbial community.

Polycyclic aromatic hydrocarbon (PAH) degrading bacteria often rely on laboratory enrichments and isolations. Herbst et al. performed the first in situ microcosms with 13C-naphthalene (BACTRAP(R)s) in which the BACTRAPs were exposed to a PAH-contaminated aquifer (Herbst et al. 2013). Briefly, BACTRAPs are in situ microcosms that consist of perforated Teflon tubes of 5 cm length and 1 cm diameter with a perforation of 1 mm which are filled with 1 g of preheated (4 h at 300 °C) activated carbon pellets (Biocoal, Silcarbon Aktivkohle GmbH, Kirchhundem, Germany). Following sterilization and hydration of the BACTRAPs by autoclaving at 121 °C, the BACTRAPs were each loaded with 25 mg [13C6]-naphthalene by dropping the compounds diluted in 1 mL n-hexane on the activated carbon pellets. As main result of this experiment, Burkholderiales, Actinomycetales, and Rhizobiales were the most active microorganisms in the groundwater communities; and the naphthalene degradation pathway showed high 13C incorporation (about 50 atom%). This study convincingly demonstrated that a combination of in situ microcosms with protein-SIP is a suitable tool for the identification of metabolic key players as well as degradation pathways.

In another approach, the microbial processes in constructed wetlands (CWs) under controlled conditions were investigated. CWs are well-established treatment systems for the bioremediation of contaminated waste- and groundwaters (Schroder et al. 2007). While the systems provide high removal efficiencies for numerous organic contaminants, the microbial processes are not yet well known. Plant roots stimulate the microbial degradation activity within the CW by the exudation of organic compounds as well as oxygen (Lagos et al. 2015). Detailed knowledge about the microbial key players and degradation pathways can aid the design of CWs for improved and stable performances. A planted fixed bed reactor (PFR) was designed and operated in a greenhouse (Kappelmeyer et al. 2002). Toluene was added to the inflow to investigate the respective degradation processes. The microbial community composition was assessed by 16S rRNA gene sequencing and protein-SIP by pulsed addition of 13C-labelled toluene and revealed fast degradation during 40 h inside the PFR (Lünsmann et al. 2015). After 20 h the 13C label was detected in bacterial proteins; almost all labelled proteins could be assigned to the order of Burkholderiales, which constituted to only about 20% of the microbial community (Lünsmann et al. 2015). Among them, two bacterial families showed different proportions of 13C-incorporation (RIA) in their proteins, leading to the conclusion that Burkholderiaceae derived more biomass from toluene (73% RIA), than the Comamonadaceae (64% RIA) (Lünsmann et al. 2015). The complete pathway of toluene degradation was retrieved by protein-SIP which demonstrated that toluene degradation was initiated by a monooxygenase yielding p-cresol, followed by a phenol hydroxylase introducing a second hydroxyl functionality group. It became evident that toluene-derived carbon might be fully oxidized via the citric acid cycle or could be stored by the microbes anabolically as polyhydroxyalkanoate (PHA) granules (Lünsmann et al. 2015, 2016). Toluene degradation is not limited by oxygen availability despite the applied concentrations in the PFR, and Burkholderiales may serve as indicators for effective hydrocarbon removal at low oxygen concentrations.

Micro-pollutants [herbicides, pesticides, and pharmaceuticals present in the environment at very low concentrations (ng-μg/L)] have emerged as an important topic in the field of environmental microbiology (Luo et al. 2014). The most important consequence of the low concentration is that these compounds are often co-metabolized (Fischer and Majewsky 2014; Kjeldal et al. 2016). By designing a protein-SIP experiment, the used concentration has to be remarkably exceeding the environmentally relevant concentration to obtain adequate stable isotope incorporation. If the compounds are only co-metabolically transformed, the incorporation into the biomass of the microorganisms will not be enough, and a protein-SIP experiment using the labelled micro-pollutant is not suitable. Still, using labelled water as substrate-independent compound may be a promising approach.

Hydrogen has the highest abundance in proteins, but its application was limited due to a fast H/D-exchange, which will occur in living cells and during sample preparation. In addition, deuterium at high concentrations inhibits enzymes due to kinetic isotope effects. Furthermore, the chromatographic properties of deuterated compounds that originate from the higher hydrophilicity of CD-bonds compared to CH-bonds (Boersema et al. 2009), cause significant changes in the retention time (Zhang et al. 2002). However, the usage of heavy water, labelled with either deuterium or 18-oxygen, has advances and is promising as it assesses the global activity regardless of the used substrate. Some applications successfully demonstrated using D2O in acid mine drainage biofilms (Justice et al. 2014) and soil communities using H218O in DNA (Blazewicz and Schwartz 2011; Schwartz 2009; Schwartz 2007) and RNA (Angel and Conrad 2013; Rettedal and Brozel 2015). Protein-SIP was applied to track the usage of 15NH4 or deuterium oxide for different members of the community (Justice et al. 2014). There were relatively few 15N-enriched archaeal proteins, and all showed low atom percent enrichment consistent with Archaea synthesizing protein using the predominantly 14N biomass derived from recycled biomolecules (Justice et al. 2014). Deuterium oxide was used to detect general microbial activity in the samples. Interestingly, bacterial species showed only little protein synthesis using deuterium oxide which reflects that the exclusive ability of Archaea to synthesize proteins using 2H2O perhaps due to archaeal heterotrophy (Justice et al. 2014). In a combined Raman microspectroscopy, metaproteomics, and protein-SIP approach, the complex metabolic response using D2O labelling allowed the monitoring of metabolic activity combined with a functional characterization of active populations isolates from groundwater (Taubert et al. 2018). In particular, 18-oxygen seems more promising than deuterium as the abiotic HD exchange of acidic hydrogens could cause a dilution of the label once the proteins are in contact with unlabelled water (Englander and Kallenbach 1983).

2 Conclusions

Metaproteomics of environmental samples is generally limited by three factors: first, the extraction of proteomes (with various biomass availabilities) from samples with background substances; second, the low genetic coverage in protein-coding databases; and third, the high complexity of the microbial community. For the last factor, protein-SIP contributes to an increase of the coverage because it points directly toward the detection of the active members within a community in respect to a specific substrate. Since the introduction of protein-SIP in 2008, all isotopes present in proteins have been used for studies. Thereby, a broad variety of studies based on energy flux (carbon), nitrogen utilization (nitrogen), or markers of general metabolic activity (deuterium and sulfur) have been performed. The latter examples underline the unique potential of protein-SIP for analyzing microbial communities in situ in soil, sediments, aquifers, water bodies, and higher organisms. Still, one of the urgent needs is the development of new de novo approaches where the natural peptide information is not necessary. The identification of the amino acid composition from metabolically labelled isotopes in the MS spectra is generally performed by matching theoretical MS spectra to protein-coding sequence databases. Bioinformatics algorithms rely on monoisotopic precursor mass (natural abundance) for identification; likewise, MetaProSIP relies on the natural abundance precursor for identification and calculation of the dynamic mass shift starting from that.

References

  1. Afgan E, Baker D, Batut B, van den Beek M, Bouvier D, Cech M, Chilton J, Clements D, Coraor N, Gruning BA, Guerler A, Hillman-Jackson J, Hiltemann S, Jalili V, Rasche H, Soranzo N, Goecks J, Taylor J, Nekrutenko A, Blankenberg D (2018) The galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update. Nucleic Acids Res 46(W1):W537–W544CrossRefGoogle Scholar
  2. Angel R, Conrad R (2013) Elucidating the microbial resuscitation cascade in biological soil crusts following a simulated rain event. Environ Microbiol 15(10):2799–2815Google Scholar
  3. Bastida F, Jehmlich N, Lima K, Morris BE, Richnow HH, Hernandez T, von Bergen M, Garcia C (2015) The ecological and physiological responses of the microbial community from a semiarid soil to hydrocarbon contamination and its bioremediation using compost amendment. J Proteome 135:162–169CrossRefGoogle Scholar
  4. Blazewicz SJ, Schwartz E (2011) Dynamics of (1)(8)O incorporation from H(2) (1)(8)O into soil microbial DNA. Microb Ecol 61(4):911–916CrossRefGoogle Scholar
  5. Boersema PJ, Raijmakers R, Lemeer S, Mohammed S, Heck AJ (2009) Multiplex peptide stable isotope dimethyl labeling for quantitative proteomics. Nat Protoc 4(4):484–494CrossRefGoogle Scholar
  6. Chen X, Wei S, Ji Y, Guo X, Yang F (2015) Quantitative proteomics using SILAC: principles, applications, and developments. Proteomics 15(18):3175–3192CrossRefGoogle Scholar
  7. Englander SW, Kallenbach NR (1983) Hydrogen exchange and structural dynamics of proteins and nucleic acids. Q Rev Biophys 16(4):521–655CrossRefGoogle Scholar
  8. Fischer K, Majewsky M (2014) Cometabolic degradation of organic wastewater micropollutants by activated sludge and sludge-inherent microorganisms. Appl Microbiol Biotechnol 98(15):6583–6597CrossRefGoogle Scholar
  9. Grob C, Taubert M, Howat AM, Burns OJ, Dixon JL, Richnow HH, Jehmlich N, von Bergen M, Chen Y, Murrell JC (2015) Combining metagenomics with metaproteomics and stable isotope probing reveals metabolic pathways used by a naturally occurring marine methylotroph. Environ Microbiol 17(10):4007–4018CrossRefGoogle Scholar
  10. Herbst FA, Bahr A, Duarte M, Pieper DH, Richnow HH, von Bergen M, Seifert J, Bombach P (2013) Elucidation of in situ polycyclic aromatic hydrocarbon degradation by functional metaproteomics (protein-SIP). Proteomics 13(18–19):2910–2920Google Scholar
  11. Herbst FA, Lunsmann V, Kjeldal H, Jehmlich N, Tholey A, von Bergen M, Nielsen JL, Hettich RL, Seifert J, Nielsen PH (2015) Enhancing metaproteomics – the value of models and defined environmental microbial systems. Proteomics 16(5):783–798CrossRefGoogle Scholar
  12. Jehmlich N, Schmidt F, Hartwich M, von Bergen M, Richnow HH, Vogt C (2008a) Incorporation of carbon and nitrogen atoms into proteins measured by protein-based stable isotope probing (protein-SIP). Rapid Commun Mass Spectrom 22(18):2889–2897CrossRefGoogle Scholar
  13. Jehmlich N, Schmidt F, von Bergen M, Richnow HH, Vogt C (2008b) Protein-based stable isotope probing (protein-SIP) reveals active species within anoxic mixed cultures. ISME J 2(11):1122–1133CrossRefGoogle Scholar
  14. Jehmlich N, Vogt C, Lunsmann V, Richnow HH, von Bergen M (2016) Protein-SIP in environmental studies. Curr Opin Biotechnol 41:26–33CrossRefGoogle Scholar
  15. Justice NB, Li Z, Wang Y, Spaudling SE, Mosier AC, Hettich RL, Pan C, Banfield JF (2014) (15)N- and (2)H proteomic stable isotope probing links nitrogen flow to archaeal heterotrophic activity. Environ Microbiol 16(10):3224–3237CrossRefGoogle Scholar
  16. Kappelmeyer U, Wießner A, Kuschk P, Kästner M (2002) Operation of a universal test unit for planted soil filters – planted fixed bed reactor. Eng Life Sci 2(10):311–315CrossRefGoogle Scholar
  17. Kjeldal H, Zhou NA, Wissenbach DK, von Bergen M, Gough HL, Nielsen JL (2016) Genomic, proteomic, and metabolite characterization of gemfibrozil-degrading organism Bacillus sp. GeD10. Environ Sci Technol 50(2):744–755CrossRefGoogle Scholar
  18. Lagos L, Maruyama F, Nannipieri P, Mora M, Ogram A, Jorquera M (2015) Current overview on the study of bacteria in the rhizosphere by modern molecular techniques: a mini–review. J Soil Sci Plant Nutr 15(2):504–523Google Scholar
  19. Lünsmann V, Kappelmeyer U, Benndorf R, Martinez-Lavanchy PM, Taubert A, Adrian L, Duarte M, Pieper DH, von Bergen M, Muller JA, Heipieper HJ, Jehmlich N (2015) In-situ protein-SIP highlights Burkholderiaceae as key players degrading toluene by para ring hydroxylation in a constructed wetland model. Environ Microbiol 18(4):1176–1186CrossRefGoogle Scholar
  20. Lünsmann V, Kappelmeyer U, Taubert A, Nijenhuis I, von Bergen M, Heipieper HJ, Muller JA, Jehmlich N (2016) Aerobic toluene degraders in the rhizosphere of a constructed wetland model show diurnal polyhydroxyalkanoate metabolism. Appl Environ Microbiol 82(14):4126–4132CrossRefGoogle Scholar
  21. Luo Y, Guo W, Ngo HH, Nghiem LD, Hai FI, Zhang J, Liang S, Wang XC (2014) A review on the occurrence of micropollutants in the aquatic environment and their fate and removal during wastewater treatment. Sci Total Environ 473-474:619–641CrossRefGoogle Scholar
  22. Pan C, Fischer CR, Hyatt D, Bowen BP, Hettich RL, Banfield JF (2011) Quantitative tracking of isotope flows in proteomes of microbial communities. Mol Cell Proteomics 10(4):M110 006049CrossRefGoogle Scholar
  23. Rettedal EA, Brozel VS (2015) Characterizing the diversity of active bacteria in soil by comprehensive stable isotope probing of DNA and RNA with H218 O. Microbiology 4(2):208–219Google Scholar
  24. Sachsenberg T, Herbst FA, Taubert M, Kermer R, Jehmlich N, von Bergen M, Seifert J, Kohlbacher O (2015) MetaProSIP: automated inference of stable isotope incorporation rates in proteins for functional metaproteomics. J Proteome Res 14(2):619–627CrossRefGoogle Scholar
  25. Schroder P, Navarro-Avino J, Azaizeh H, Goldhirsh AG, DiGregorio S, Komives T, Langergraber G, Lenz A, Maestri E, Memon AR, Ranalli A, Sebastiani L, Smrcek S, Vanek T, Vuilleumier S, Wissing F (2007) Using phytoremediation technologies to upgrade waste water treatment in Europe. Environ Sci Pollut Res Int 14(7):490–497CrossRefGoogle Scholar
  26. Schwartz E (2007) Characterization of growing microorganisms in soil by stable isotope probing with (H2O)-O-18. Appl Environ Microbiol 73(8):2541–2546CrossRefGoogle Scholar
  27. Schwartz E (2009) Analyzing microorganisms in environmental samples using stable isotope probing with H2(18)O. Cold Spring Harb Protoc 2009(12):pdbprot5341CrossRefGoogle Scholar
  28. Taubert M, Jehmlich N, Vogt C, Richnow HH, Schmidt F, von Bergen M, Seifert J (2011) Time resolved protein-based stable isotope probing (protein-SIP) analysis allows quantification of induced proteins in substrate shift experiments. Proteomics 11(11):2265–2274CrossRefGoogle Scholar
  29. Taubert M, Vogt C, Wubet T, Kleinsteuber S, Tarkka MT, Harms H, Buscot F, Richnow HH, von Bergen M, Seifert J (2012) Protein-SIP enables time-resolved analysis of the carbon flux in a sulfate-reducing, benzene-degrading microbial consortium. ISME J 6(12):2291–2301CrossRefGoogle Scholar
  30. Taubert M, Stockel S, Geesink P, Girnus S, Jehmlich N, von Bergen M, Rosch P, Popp J, Kusel K (2018) Tracking active groundwater microbes with D2 O labelling to understand their ecosystem function. Environ Microbiol 20(1):369–384CrossRefGoogle Scholar
  31. von Bergen M, Jehmlich N, Taubert M, Vogt C, Bastida F, Herbst FA, Schmidt F, Richnow HH, Seifert J (2013) Insights from quantitative metaproteomics and protein-stable isotope probing into microbial ecology. ISME J 7(10):1877–1885CrossRefGoogle Scholar
  32. Zhang R, Sioma CS, Thompson RA, Xiong L, Regnier FE (2002) Controlling deuterium isotope effects in comparative proteomics. Anal Chem 74(15):3662–3669CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Molecular Systems BiologyHelmholtz Centre for Environmental Research – UFZLeipzigGermany

Personalised recommendations